Learning coordination policies over heterogeneous graphs for human-robot teams via recurrent neural schedule propagation

A method comprising:

• executing, by a processor, a graph network configured to encode information associated with resources and tasks;
• and
• executing, by the processor, a recurrent decoder configured to receive an output of the graph network to determine a schedule while accounting for one or more spatiotemporal constraints established by the graph network, wherein the recurrent decoder is configured to generate consequential models of each task-agent assignments based on an initial model, and
• wherein the schedule is employed to control or monitor one or more robotic systems.

A non-transitory computer-readable medium having instructions stored thereon, wherein execution of the instruction by a processor causes the processor to:

• execute a graph network configured to encode information associated with resources and tasks;
• and
• execute a recurrent decoder configured to receive an output of the graph network to determine a schedule while accounting for one or more spatiotemporal constraints established by the graph network, wherein the recurrent decoder is configured to generate consequential models of each task-agent assignments based on an initial model, and
• wherein the schedule is employed to control or monitor one or more robotic systems.